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Best Real Estate Leads To Buy

DATABASE ID: LOCAL-BEST | RECORDS: 50,000 | LAST VERIFIED: TODAY
IDCompanyWebsiteKey ContactTitleVerified EmailIntent Score
00001Company 411 LLCwww.company1.comExec Name 1CEOexec1@company1.com97
00002Company 405 LLCwww.company2.comExec Name 2Founderexec2@company2.com85
00003Company 144 LLCwww.company3.comExec Name 3VP of Marketingexec3@company3.com90
00004Company 633 LLCwww.company4.comExec Name 4Founderexec4@company4.com94
00005Company 63 LLCwww.company5.comExec Name 5CEOexec5@company5.com80
00006Company 265 LLCwww.company6.comExec Name 6VP of Marketing••••••••••••@••••.com95
00007Company 13 LLCwww.company7.comExec Name 7CEO••••••••••••@••••.com89
00008Company 212 LLCwww.company8.comExec Name 8Founder••••••••••••@••••.com99
00009Company 356 LLCwww.company9.comExec Name 9VP of Marketing••••••••••••@••••.com82
00010Company 362 LLCwww.company10.comExec Name 10Founder••••••••••••@••••.com82
00011Company 629 LLCwww.company11.comExec Name 11CEO••••••••••••@••••.com82
00012Company 49 LLCwww.company12.comExec Name 12VP of Marketing••••••••••••@••••.com86
00013Company 264 LLCwww.company13.comExec Name 13CEO••••••••••••@••••.com81
00014Company 833 LLCwww.company14.comExec Name 14Founder••••••••••••@••••.com99
00015Company 600 LLCwww.company15.comExec Name 15VP of Marketing••••••••••••@••••.com87
00016Company 778 LLCwww.company16.comExec Name 16Founder••••••••••••@••••.com86
00017Company 341 LLCwww.company17.comExec Name 17CEO••••••••••••@••••.com99
00018Company 635 LLCwww.company18.comExec Name 18VP of Marketing••••••••••••@••••.com85
00019Company 342 LLCwww.company19.comExec Name 19CEO••••••••••••@••••.com91
00020Company 280 LLCwww.company20.comExec Name 20Founder••••••••••••@••••.com93
00021Company 563 LLCwww.company21.comExec Name 21VP of Marketing••••••••••••@••••.com81
00022Company 14 LLCwww.company22.comExec Name 22Founder••••••••••••@••••.com89
00023Company 714 LLCwww.company23.comExec Name 23CEO••••••••••••@••••.com99
00024Company 912 LLCwww.company24.comExec Name 24VP of Marketing••••••••••••@••••.com91
00025Company 573 LLCwww.company25.comExec Name 25CEO••••••••••••@••••.com85
00026Company 535 LLCwww.company26.comExec Name 26Founder••••••••••••@••••.com83
00027Company 533 LLCwww.company27.comExec Name 27VP of Marketing••••••••••••@••••.com89
00028Company 950 LLCwww.company28.comExec Name 28Founder••••••••••••@••••.com87
00029Company 651 LLCwww.company29.comExec Name 29CEO••••••••••••@••••.com87
00030Company 514 LLCwww.company30.comExec Name 30VP of Marketing••••••••••••@••••.com97
00031Company 76 LLCwww.company31.comExec Name 31CEO••••••••••••@••••.com83
00032Company 642 LLCwww.company32.comExec Name 32Founder••••••••••••@••••.com96
00033Company 69 LLCwww.company33.comExec Name 33VP of Marketing••••••••••••@••••.com98
00034Company 511 LLCwww.company34.comExec Name 34Founder••••••••••••@••••.com93
00035Company 953 LLCwww.company35.comExec Name 35CEO••••••••••••@••••.com91
00036Company 814 LLCwww.company36.comExec Name 36VP of Marketing••••••••••••@••••.com96
00037Company 350 LLCwww.company37.comExec Name 37CEO••••••••••••@••••.com96
00038Company 420 LLCwww.company38.comExec Name 38Founder••••••••••••@••••.com87
00039Company 661 LLCwww.company39.comExec Name 39VP of Marketing••••••••••••@••••.com82
00040Company 408 LLCwww.company40.comExec Name 40Founder••••••••••••@••••.com91
00041Company 344 LLCwww.company41.comExec Name 41CEO••••••••••••@••••.com84
00042Company 142 LLCwww.company42.comExec Name 42VP of Marketing••••••••••••@••••.com90
00043Company 369 LLCwww.company43.comExec Name 43CEO••••••••••••@••••.com89
00044Company 688 LLCwww.company44.comExec Name 44Founder••••••••••••@••••.com81
00045Company 311 LLCwww.company45.comExec Name 45VP of Marketing••••••••••••@••••.com83
00046Company 100 LLCwww.company46.comExec Name 46Founder••••••••••••@••••.com82
00047Company 578 LLCwww.company47.comExec Name 47CEO••••••••••••@••••.com90
00048Company 338 LLCwww.company48.comExec Name 48VP of Marketing••••••••••••@••••.com91
00049Company 174 LLCwww.company49.comExec Name 49CEO••••••••••••@••••.com92
00050Company 786 LLCwww.company50.comExec Name 50Founder••••••••••••@••••.com86
[ 49,950 ROWS LOCKED ]
>_ SYSTEM.ANALYSIS.LOG_

Scraping real estate leads is a mess. Most sources are either gated behind captchas or stale by the time you get them. We spent 3 months cracking the MLS feeds and cross-referencing them with public records. The result? Every row is a property that's actively listed or recently sold, with the owner's contact info when available. We skipped the FSBO spam and the 'we buy houses' flippers. This is the raw material for a cold email campaign that actually converts.

The hardest part was deduplication. Same property listed on Zillow, Realtor.com, and the broker's site. We built a fuzzy match on address + zip, then kept the freshest listing. Also had to filter out the 'coming soon' listings that never hit the market. The data is clean, but it's not perfect. If you find a duplicate, keep it. It's a freebie.

We're pulling from county tax assessor records, public MLS scrape, and a few off-market sources we won't name. The tax records give us the owner's name and mailing address. The MLS gives us the listing price and days on market. We merge them and flag the ones with a high probability of a motivated seller (e.g., expired listings, price drops, absentee owners). You're welcome.

Data Schema & Dictionary

property_address

Full street address of the property. This is the key to everything. If you're doing direct mail, this is the envelope.

city_state_zip

City, state, and zip code split into separate columns. Why? Because you'll want to filter by market. Don't make me explain basic segmentation.

owner_name

Name of the legal owner as per tax records. Sometimes it's a trust or LLC. That's still useful. You'll need to do a bit of digging to find a phone number, but the name gets you past the gatekeeper.

owner_mailing_address

The address where the owner gets their tax bill. Often different from the property address. This is your direct mail target. If they're an absentee owner, this is gold.

property_type

Single-family, condo, townhouse, etc. Helps you tailor your pitch. No one wants to send a 'we buy houses' letter to a condo owner in a high-rise.

estimated_value

Our algorithmic estimate of the property's market value. Not a Zestimate. It's based on recent comps and tax assessments. Good for filtering out low-value junk.

mortgage_amount

The outstanding mortgage balance if we could pull it. Sometimes blank. If it's high, the owner has equity but might be underwater. If it's low, they might be a good candidate for a sale.

days_on_market

How many days the listing has been active. The longer, the more desperate the seller. Filter for >90 days if you want the motivated ones.

last_sale_date

The date the property last sold. If it's recent, they might not be ready to move. If it's old, they might have paid off the mortgage. Use it to gauge equity.

last_sale_price

The price paid at the last sale. Compare to the estimated value to see how much equity they might have. That's your angle for a wholesale deal.

listing_price

The current asking price if it's listed. If it's been reduced, that's a signal. We include a price reduction flag in another column.

price_reduced

Boolean: TRUE if the listing price has been cut at least once. A price cut is a scream for help. Use it.

days_since_last_update

Days since the listing was last touched. A stale listing means the agent has checked out. That's your opening.

owner_phone

We scraped this from public records and some sketchy but legal sources. It's hit or miss. When it's there, you can skip the skip tracing. When it's not, you'll have to work for it.

owner_email

Same deal as the phone. We got it from tax records or the agent's website. Don't expect a 100% hit rate. But when it's there, it's a direct line.

list_agent_name

The listing agent's name. If you're looking to network or poach listings, this is your target. Also useful to check if they're a discount broker.

list_agent_phone

The listing agent's phone number. For when you want to make a lowball offer directly.

list_agent_email

The listing agent's email. Use it for a polite intro or a not-so-polite lowball. Your call.

off_market_flag

TRUE if the property is not currently listed but we have reason to believe the owner might sell (e.g., expired listing, absentee owner). This is your hidden gem column.

absentee_owner_flag

TRUE if the owner's mailing address is different from the property address. Means they don't live there. Often a landlord or an inherited property. More likely to sell.

equity_score

A score from 0-100 indicating the estimated equity as a percentage of property value. We calculate it from mortgage balance vs estimated value. High score = high equity = better for a wholesale deal.

motivation_score

A score from 0-100 based on signals like price reductions, days on market, absentee ownership, and expired listings. Higher = more likely to say yes to a lowball. Sort by this column and start calling.

Frequently Asked Questions

How fresh is this data?
We scrape the MLS and tax records on the 1st and 15th of every month. The data you get is at most 2 weeks old. If you find a listing that's been sold for a month, tell us and we'll swap the credit. Simple.
Can I get a sample before buying?
Yeah, just email me and I'll send you 50 random rows. No email drip, no sales call. If you like it, buy. If not, move on. I hate the 'let's hop on a call' dance as much as you do.
How many records are in the full database?
Right now, about 2.3 million properties across the US. We add new ones every month and remove the ones that just sold. It's not a static file. You get the latest pull.
Is this legal?
We only scrape public records and MLS data that's either licensed or publicly accessible. No hacks, no black hat stuff. You're responsible for how you use it—CAN-SPAM, TCPA, etc. We're not your lawyer.
What format is the data in?
CSV. One row per property. Columns are in the schema doc we send you. If you need Excel, open the CSV in Excel. Don't ask me for a .xlsx.
Can I get a refund?
If the data is bad—like, the addresses are wrong or the owner names are garbage—we'll refund you within 30 days. But if you just bought it and didn't like the color of the CSV, that's on you.

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